1. Technical Field
Present invention embodiments relate to masking data, and more specifically, to masking data objects consistently across a plurality of different data resources to protect privacy.
2. Discussion of the Related Art
Data privacy is a concern for enterprises around the world, Collection, disclosure, and protection of consumers' nonpublic personal information or personally identifiable information (e.g., medical history, financial information, etc.) are governed by a range of laws and regulations (e.g., the Gramm-Leach Bliley Act; the Health Insurance Portability and Accountability Act; the European Union Data Protection Directive; privacy laws in Canada, Japan, and Australia; the Payment Card industry Data Security Standard; the Interagency Guidelines for Safeguarding Customer Information; Basel II operational controls and Sarbanes-Oxley internal controls; etc.).
To address these concerns, data masking capabilities are embedded in most commercially available Extract, Transform, and Load (ETL) and Test Data Management (TDM) products. Some database products and application software (e.g., enterprise resource planning (ERP) applications, customer relationship management (CRM) applications, human capital management (HCM) applications, etc.) also include data masking capabilities. In addition, point solutions have been developed to fill particular needs. Many companies build their own data masking solution to fit their situation if they can find no other appropriate tool.
Many large enterprises employ dozens of mission critical software applications, of which some are commercial, off the shelf applications while others are customer-created. These applications may share account information about the company's clients, products, and services, which may be subject to masking. The applications may interact with each other. In addition, an end-user may view the data using more than one of the applications. When the applications are used with a varied set of operating systems and data sources, an enterprise may have to piece together a data masking strategy from various niche and/or custom solutions. These disparate solutions will use different algorithms, resulting in inconsistently masked data.